Development of a high-altitude renal disease diagnostic model based on machine learning and multiple biomarker detection: a retrospective study of 19,068 patients

Machine learning models significantly improve diagnostic accuracy of CKD in high-altitude populations. ANN and RF models demonstrated promising predictive capability, supporting their potential clinical application in high-altitude regions.

Authors

Institutions

Publication Details

Journal
BMC Nephrology
Published
2026-03-26
DOI
https://doi.org/10.1186/s12882-026-04812-5
Primary Topic
High Altitude and Hypoxia
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of a high-altitude renal disease diagnostic model based on machine learning and multiple biomarker detection: a retrospective study of 19,068 patients

Sergio Benardini, Xinyao Ji, Wei Yan, Yuheng Xiao et al.
BMC Nephrology
High Altitude and Hypoxia
article

Development of a high-altitude renal disease diagnostic model based on machine learning and multiple biomarker detection: a retrospective study of 19,068 patients

Sergio Benardini, Xinyao Ji, Wei Yan, Yuheng Xiao, Chenhao Liu, Changchun Niu, Yuli Luo, Feng Zheng, Zi-an Li, Menglin Luo
article en

Abstract

Machine learning models significantly improve diagnostic accuracy of CKD in high-altitude populations. ANN and RF models demonstrated promising predictive capability, supporting their potential clinical application in high-altitude regions.

BMC Nephrology
University of Rome Tor Vergata (IT), Army Medical University (CN), Dalian Medical University (CN), Qinghai No.3 People's Hospital (CN), Second Affiliated Hospital of Chongqing Medical University (CN), Chongqing Emergency Medical Center (CN), Qinghai Provincial Peoples Hospital (CN), First People's Hospital of Chongqing (CN), Chongqing Medical University (CN)
Good health and well-being
Openalex Percentile: Top 6%
High Altitude and Hypoxia
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Development of a high-altitude renal disease diagnostic model based on machine learning and multiple biomarker detection: a retrospective study of 19,068 patients — Sergio Benardini, Xinyao Ji, et al. · BMC Nephrology (2026) | TGRS Research Map | TGRS